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An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid

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An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid energies Article An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid Sajjad Ali 1,2 , Imran Khan 3 , Sadaqat Jan 4 and Ghulam Hafeez 3,5,* 1 Department of Telecommunication Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; [email protected] 2 Department of Telecommunication Engineering, University of Engineering and Technology, Mardan 23200, Pakistan 3 Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan; [email protected] 4 Department of Computer Software Engineering, University of Engineering and Technology, Mardan 23200, Pakistan; [email protected] 5 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 44000, Pakistan * Correspondence: [email protected] or [email protected]; Tel.: +92-300-5003574 or +92-348-8818497 Abstract: Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts Citation: An Optimization Based consumer power usage patterns using real-time pricing signals and generation from utility and Power Usage Scheduling Strategy photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate Using Photovoltaic-Battery System peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid for Demand-Side Management in Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling Smart Grid. Energies 2021, 14, 2201. model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and https://doi.org/10.3390/en14082201 with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of Academic Editor: Herodotos Herodotou using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of Received: 18 March 2021 the proposed framework comparatively with existing frameworks, simulations are conducted. The Accepted: 6 April 2021 results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and Published: 15 April 2021 peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Publisher’s Note: MDPI stays neutral Thus, the proposed HGACO algorithm-based energy management framework outperforms existing with regard to jurisdictional claims in frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) published maps and institutional affil- algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm. iations. Keywords: energy management; battery energy storage systems; photovoltaic; demand response; scheduling; smart grid Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article 1. Introduction distributed under the terms and Electrical energy is one of the most indispensable needs of human life. Developing conditions of the Creative Commons countries cannot optimally meet this basic need for residents due to limited financial Attribution (CC BY) license (https:// budgets and scarce generating stations. The electric utility companies involuntary move creativecommons.org/licenses/by/ 4.0/). towards load shedding to partially satisfy their consumers. However, load shedding is not Energies 2021, 14, 2201. https://doi.org/10.3390/en14082201 https://www.mdpi.com/journal/energies Energies 2021, 14, 2201 2 of 29 a solution because it causes frustration to consumers. Considering the above limitations, one viable solution is Demand Side Management (DSM) via consumer load scheduling. Optimal DSM is only possible by actively engaging consumers in the DR programs via the two-way communication infrastructure of a Smart Grid (SG). Moreover, current power- generating plants are operated on conventional sources, which are limited, scarce, and expensive and cause pollutant emissions that lead to climate change. Hence, the Renewable Energy Sources (RESs) are an alternative solution that is abundant, cheap, environment friendly, and continually replenished [1]. In the literature, three approaches are adopted to perform optimal DSM in SG via DR programs: mathematical methods, game theory-based methods, and heuristic algorithms. The mathematical techniques are classified into two classes, namely, deterministic and stochastic methods. The difference between stochastic and deterministic methods is the initialization of the initial solution, deterministic methods generate the same initial solution when addressing the same problem, and stochastic methods randomly initialize solutions that permit various solutions for the given problem in each run [2]. A novel modular modelling Energy Management System (EMS) is developed for urban multi-energy sys- tems [3]. To evaluate the proposed EMS’s saving potential, an extensive case study is conducted compared to the traditional control strategies. It can be evident from the results that an annual cost-saving potential between 3 and 6 percent can be attained when the proposed EMS model is used in combination with additional components such as battery and thermal energy storage. However, there are still no open source solvers available to handle large-scale Mixed Integer Linear Programming (MILP). Furthermore, in large-scale EMS applications (e.g., city districts) such as IBM ILOG CPLEX or GUROBI, the monetary benefit is usually high enough to justify commercial solvers’ costs. A Mixed Integer Lin- ear Programming (MILP)-based scheme is introduced for energy-efficient management in SG [4]. The developed scheme optimally plans smart devices and the charging and discharging of electric vehicles to reduce energy costs. In the developed model, users can produce their energy from microgrids containing wind turbines, solar panels, and Energy Storage System (ESS). The results confirm that the proposed MILP-based energy management is more effective and productive than legacy models. However, how to handle the intermittent nature of renewable energy sources is not discussed. A combina- tion of Sequential Quadratic Programming algorithm (SQP) and Binary Particle Swarm Optimization (BPSO)-based optimization model is proposed in [5]. The proposed com- bination model is applied for energy management of the residential sector. The results validated that the proposed model performed energy management efficiently. However, for reliability in DR programming, the irregular and indeterminate renewable energy characteristics will raise substantial challenges. Thus, to improve the reliability of the renewable energy uncertainty, an appropriate uncertainty processing technology such as chance-constrained technique and Model Predictive Control (MPC) can be incorporated into the MINLP model presented in the proposed work. The authors in [6] proposed a smart home energy management system model including power generation, solar panels, small wind turbine, battery, and appliances. The proposed model efficiently schedules household thermal and electrical appliances using time-varying pricing to minimize finan- cial expenses and to ensure peak demand clipping. A DR program is employed at different levels [7]: single home, combined home, network level, and market level for consumers to actively participate in DSM. Electric Vehicles (EVs), Energy Storage Systems (ESSs), and RESs are actively engaged in the DR program in each scenario. The mathematical formulation of each level is implemented with uncertainty consideration. However, the technology barriers such as sensing, controlling, monitoring, and communication infras- tructure and markets such as policies, regulation, and structure are not considered. The authors considered the DSM of a home including smart appliances, EVs, ESS, and PV microgeneration in [8]. The resources and loads are coordinated using indexed pricing models for DSM to maximize self-generation and to minimize cost by reducing utility purchase. However, an investigation on minimum software and hardware requirements for Energies 2021, 14, 2201 3 of 29 the unit hosting the Smart Home Controller (SHC) module (an issue that is strictly related to the task of developing efficient and tailored solution algorithms) and a comprehensive and detailed cost–benefit analysis of the proposed residential EMS were performed using approaches such as the ones presented to generate highly realistic sequences of events. EMS’s modular design for a grid-connected battery-based microgrid was presented in [9]. The developed model was for power generation-side using MILP to handle charging and discharging of batteries, to encourage self-consumption, and to reduce operating costs. The proposed model has been tested and experimentally validated at Alborg University at the Microgrid
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